def main():
results = {
"results": [],
"measure_reg": [],
"measure_td": [],
"measure_mc": [],
}
hps = {
"opt": ARGS.opt,
"env_name": ARGS.env_name,
"lr": ARGS.learning_rate,
"weight_decay": ARGS.weight_decay,
"run": ARGS.run,
}
nhid = hps.get("nhid", 32)
gamma = hps.get("gamma", 0.99)
mbsize = hps.get("mbsize", 32)
weight_decay = hps.get("weight_decay", 0)
sample_near = hps.get("sample_near", "both")
slice_size = hps.get("slice_size", 0)
env_name = hps.get("env_name", "ms_pacman")
clone_interval = hps.get("clone_interval", 10_000)
reset_on_clone = hps.get("reset_on_clone", False)
reset_opt_on_clone = hps.get("reset_opt_on_clone", False)
max_clones = hps.get("max_clones", 2)
target = hps.get("target", "last") # self, last, clones
replay_type = hps.get("replay_type", "normal") # normal, prioritized
final_epsilon = hps.get("final_epsilon", 0.05)
num_exploration_steps = hps.get("num_exploration_steps", 500_000)
lr = hps.get("lr", 1e-4)
num_iterations = hps.get("num_iterations", 10_000_000)
buffer_size = hps.get("buffer_size", 250_000)
seed = hps.get("run", 0) + 1_642_559 # A large prime number
hps["_seed"] = seed
torch.manual_seed(seed)
np.random.seed(seed)
rng = np.random.RandomState(seed)
env = AtariEnv(env_name)
num_act = env.num_actions
def make_opt(theta):
if hps.get("opt", "sgd") == "sgd":
return torch.optim.SGD(theta, lr, weight_decay=weight_decay)
elif hps["opt"] == "msgd":
return torch.optim.SGD(
theta, lr, momentum=hps.get("beta", 0.99), weight_decay=weight_decay)
elif hps["opt"] == "rmsprop":
return torch.optim.RMSprop(theta, lr, weight_decay=weight_decay)
elif hps["opt"] == "adam":
return torch.optim.Adam(theta, lr, weight_decay=weight_decay)
else:
raise ValueError(hps["opt"])
# Define model
_Qarch, theta_q, Qf, _Qsemi = nn.build(
nn.conv2d(4, nhid, 8, stride=4), # Input is 84x84
nn.conv2d(nhid, nhid * 2, 4, stride=2),
nn.conv2d(nhid * 2, nhid * 2, 3),
nn.flatten(),
nn.hidden(nhid * 2 * 12 * 12, nhid * 16),
nn.linear(nhid * 16, num_act),
)
clone_theta_q = lambda: [i.detach().clone().requires_grad_() for i in theta_q]
# Pretrained parameters
theta_target = load_parameters_from_checkpoint()
# (Same) Random parameters
theta_regress = clone_theta_q()
theta_qlearn = clone_theta_q()
theta_mc = clone_theta_q()
opt_regress = make_opt(theta_regress)
opt_qlearn = make_opt(theta_qlearn)
opt_mc = make_opt(theta_mc)
# Define loss
def sl1(a, b):
d = a - b
u = abs(d)
s = d**2
m = (u < s).float()
return u * m + s * (1 - m)
td = lambda s, a, r, sp, t, w, tw=theta_q: sl1(
r + (1 - t.float()) * gamma * Qf(sp, tw).max(1)[0].detach(),
Qf(s, w)[np.arange(len(a)), a.long()],
)
obs = env.reset()
replay_buffer = ReplayBuffer(seed, buffer_size, near_strategy=sample_near)
total_reward = 0
last_end = 0
num_fill = buffer_size
num_measure = 500
_t0 = t0 = t1 = t2 = t3 = t4 = time.time()
tm0 = tm1 = tm2 = tm3 = time.time()
ema_loss = 0
last_rewards = [0]
print("Filling buffer")
epsilon = final_epsilon
replay_buffer.new_episode(obs, env.enumber % 2)
while replay_buffer.idx < replay_buffer.size - 10:
if rng.uniform(0, 1) < epsilon:
action = rng.randint(0, num_act)
else:
action = Qf(tf(obs / 255.0).unsqueeze(0), theta_target).argmax().item()
obsp, r, done, info = env.step(action)
replay_buffer.add(obs, action, r, done, env.enumber % 2)
obs = obsp
if done:
obs = env.reset()
replay_buffer.new_episode(obs, env.enumber % 2)
# Remove last episode from replay buffer, as it didn't end
it = replay_buffer.idx
curp = replay_buffer.p[it]
while replay_buffer.p[it] == curp:
replay_buffer._sumtree.set(it, 0)
it -= 1
print(f'went from {replay_buffer.idx} to {it} when deleting states')
print("Computing returns")
replay_buffer.compute_values(lambda s: Qf(s, theta_regress), num_act)
replay_buffer.compute_returns(gamma)
replay_buffer.compute_reward_distances()
print("Training regressions")
losses_reg, losses_td, losses_mc = [], [], []
loss_reg_f = lambda x, w: sl1(Qf(x[0], w), Qf(x[0], theta_target))
loss_td_f = lambda x, w: td(*x[:-1], w, theta_target)
loss_mc_f = lambda x, w: sl1(
Qf(x[0], w)[np.arange(len(x[1])), x[1].long()], replay_buffer.g[x[-1]])
losses = {
"reg": loss_reg_f,
"td": loss_td_f,
"mc": loss_mc_f,
}
measure_reg = Measures(theta_regress, losses, replay_buffer,
results["measure_reg"], mbsize)
measure_mc = Measures(theta_mc, losses, replay_buffer,
results["measure_mc"], mbsize)
measure_td = Measures(theta_qlearn, losses, replay_buffer,
results["measure_td"], mbsize)
for i in range(100_000):
sample = replay_buffer.sample(mbsize)
replay_buffer.compute_value_difference(sample, Qf(sample[0], theta_regress))
if i and not i % num_measure:
measure_reg.pre(sample)
measure_mc.pre(sample)
measure_td.pre(sample)
loss_reg = loss_reg_f(sample, theta_regress).mean()
loss_reg.backward()
losses_reg.append(loss_reg.item())
opt_regress.step()
opt_regress.zero_grad()
loss_td = loss_td_f(sample, theta_qlearn).mean()
loss_td.backward()
losses_td.append(loss_td.item())
opt_qlearn.step()
opt_qlearn.zero_grad()
loss_mc = loss_mc_f(sample, theta_mc).mean()
loss_mc.backward()
losses_mc.append(loss_mc.item())
opt_mc.step()
opt_mc.zero_grad()
replay_buffer.update_values(sample, Qf(sample[0], theta_regress))
if i and not i % num_measure:
measure_reg.post()
measure_td.post()
measure_mc.post()
if not i % 1000:
print(i, loss_reg.item(), loss_td.item(), loss_mc.item())
def main():
device = torch.device(ARGS.device)
mm.set_device(device)
results = {
"measure": [],
"parameters": [],
}
seed = ARGS.run + 1_642_559 # A large prime number
torch.manual_seed(seed)
np.random.seed(seed)
rng = np.random.RandomState(seed)
env = AtariEnv(ARGS.env_name)
mbsize = ARGS.mbsize
Lambda = ARGS.Lambda
nhid = 32
num_measure = 1000
gamma = 0.99
clone_interval = ARGS.clone_interval
num_iterations = ARGS.num_iterations
num_Q_outputs = 1
# Model
_Qarch, theta_q, Qf, _Qsemi = mm.build(
mm.conv2d(4, nhid, 8, stride=4), # Input is 84x84
mm.conv2d(nhid, nhid * 2, 4, stride=2),
mm.conv2d(nhid * 2, nhid * 2, 3),
mm.flatten(),
mm.hidden(nhid * 2 * 12 * 12, nhid * 16),
mm.linear(nhid * 16, num_Q_outputs),
)
clone_theta_q = lambda: [i.detach().clone() for i in theta_q]
theta_target = clone_theta_q()
opt = make_opt(ARGS.opt, theta_q, ARGS.learning_rate, ARGS.weight_decay)
# Replay Buffer
replay_buffer = ReplayBuffer(seed, ARGS.buffer_size)
# Losses
td = lambda s, a, r, sp, t, idx, w, tw: sl1(
r + (1 - t.float()) * gamma * Qf(sp, tw)[:, 0].detach(),
Qf(s, w)[:, 0],
)
tdL = lambda s, a, r, sp, t, idx, w, tw: sl1(
Qf(s, w)[:, 0], replay_buffer.LG[idx])
mc = lambda s, a, r, sp, t, idx, w, tw: sl1(
Qf(s, w)[:, 0], replay_buffer.g[idx])
# Define metrics
measure = Measures(
theta_q, {
"td": lambda x, w: td(*x, w, theta_target),
"tdL": lambda x, w: tdL(*x, w, theta_target),
"mc": lambda x, w: mc(*x, w, theta_target),
}, replay_buffer, results["measure"], 32)
# Get expert trajectories
rand_classes = fill_buffer_with_expert(env, replay_buffer)
# Compute initial values
replay_buffer.compute_values(lambda s: Qf(s, theta_q), num_Q_outputs)
replay_buffer.compute_returns(gamma)
replay_buffer.compute_reward_distances()
replay_buffer.compute_episode_boundaries()
replay_buffer.compute_lambda_returns(lambda s: Qf(s, theta_q), Lambda,
gamma)
# Run policy evaluation
for it in range(num_iterations):
do_measure = not it % num_measure
sample = replay_buffer.sample(mbsize)
if do_measure:
measure.pre(sample)
replay_buffer.compute_value_difference(sample, Qf(sample[0], theta_q))
loss = tdL(*sample, theta_q, theta_target)
loss = loss.mean()
loss.backward()
opt.step()
opt.zero_grad()
replay_buffer.update_values(sample, Qf(sample[0], theta_q))
if do_measure:
measure.post()
if it and clone_interval and it % clone_interval == 0:
theta_target = clone_theta_q()
replay_buffer.compute_lambda_returns(lambda s: Qf(s, theta_q),
Lambda, gamma)
if it and it % clone_interval == 0 or it == num_iterations - 1:
ps = {str(i): p.data.cpu().numpy() for i, p in enumerate(theta_q)}
ps.update({"step": it})
results["parameters"].append(ps)
with open(f'results/td_lambda_{run}.pkl', 'wb') as f:
pickle.dump(results, f)
